System design requires that system components possess an extremely high reliability, even after long periods of time and use. Many failure mechanisms can be traced to an underlying degradation process of components. When it is possible to assess and measure a component's degradation, such measures often do not provide failure time data for purposes of assessing and improving product reliability. Gathered data may include usage patterns, environment conditions, fault/error logs such as fault codes, system sensing data such as engineering push data, and machine status collected daily. Many of these variables may be potentially related to the system failures, but do not provide accurate information on degradation of components. Thus, it is difficult to assess reliability with traditional life tests that record only failure time. Further, direct observation of a component's degradation level may be impossible in some products. A relationship between component failure and amount of degradation would make it possible to use degradation models and data to make inferences and predictions about failure time.
In general, techniques used to predict remaining useful life could apply to anything that wears out for any reason, over some period of time, or other appropriate units or measurement. Examples of device components in electrical-mechanical systems, such as in a rendering device, that wear out prior to that system's typical end-of-life are as follows: fuser assembly and many fuser components such as fuser rolls, fuser fingers and fuser sensors and switches; media path feed and drive rolls, gears/motors, and drive belts; electrical sensors and connectors in electrical systems; charge devices such as the corotrons and scorotrons; photoreceptor cleaners, intermediate belt cleaner, and fuser roll cleaners; and intermediate belt in tandem systems, etc. Techniques to predict remaining useful life, or remaining useful period, could also be used in biological systems where factors such as temperature, stress, gender, or location affect remaining useful life. Such systems include, for example, human body systems such as for cancer research, animal-related systems, and plant/tree life. Remaining useful life prediction could also be used for beach and land/hill erosion estimations and weather events such as for tornadoes and hurricanes, for example.
Maintenance policies of various device components and other computer systems have recently evolved to account for predictions of component failure time. Instead of reactive firefighting following component failure, maintenance policies could utilize proactive maintenance to reduce service costs and increase the equipment's availability. Condition-based maintenance (CBM) is one such proactive maintenance policy. CBM is a decision making strategy where preventive maintenance actions are performed on devices based on the working conditions of the system and its components. Devices and components using CBM may include: rendering and printing systems, CT/MRI machines, servers and OEM systems, servers and hard drives, photoreceptors, the US Military's Joint Strike Fighter Program and the Future Combat Systems Program, and NASA-launch vehicles and spacecraft, etc. In some situations, especially when a fault or failure can be catastrophic (e.g. nuclear power plant), it is more desirable to accurately predict the chance that a machine operates without a fault or a failure up to some future time, such as the next inspection interval, given the current machine condition and past operation profile. The probability that a machine operates without a fault until the next inspection or condition monitoring interval could be a good reference for CBM assessments on appropriate inspection intervals for components.
Techniques utilized in a CBM program can be classified into two main categories: diagnostics and prognostics. Diagnostics focuses on detection; isolation, and identification of root causes when faults/failures occur. Using prognostics aids in predicting the failures or faults before they occur in order to schedule preventive maintenance, to minimize unscheduled failures, increase machine uptime and reduce service costs.
For example, in machine prognostics, there exist two main prediction types: prediction of RUL for the device and device components, and prognostics incorporating maintenance policies. A significant part of CBM decisions are based on Remaining Useful Life (RUL) estimates of a device and components. Also known as the remaining service life, the RUL is the residual life left for a system or component before failure occurs. The RUL requires accurate information about the remaining residual life of a system or component before a failure occurs, while taking into account both the current and past machine operation conditions and operation profiles. Previous RUL estimations used ad-hoc prediction experimental methodologies that lack statistical rigor.
Because no known rigorous statistical systems or methods have been developed to generate the data that shares the similar characteristics with field failure data, a reliability study based on Monte Carlo techniques for RUL prediction could not be performed. Therefore, a rigorous statistical system and methodology is needed for a reliability study to predict the RUL of computing device components such as, for example, photoreceptors. One such rigorous statistical model is the General Path Model, also known as the General Degradation Path Model. The General Path Model is used to generate simulated data that shares similar data characteristics of historical field failure data. This generated data can be used in a Monte Carlo study for RUL prediction to investigate the effects of influential factors such as suspension percentage and heavy-tailed behavior. The RUL prediction needs to be based on both the fixed-time predictors (such as the market segment) and the time-dependent covariates (e.g., dark decay, printing rate, etc.).
The Random Forest Model is another statistical analysis system that could be used to accurately estimate RUL. The Random Forest Model involves independent training decision trees, such as classification and regression trees, on a set of data points. A random forest is a “forest” of decisions trees, where each tree may be randomly perturbed during training on the data points from the other trees to produce independent trees. The Random Forest Model can also be used for accurately predicting RUL based on both fixed-time predictors and time-dependent covariates, which are both contained in the field data of photoreceptors, for example.
Therefore, a need exists for a method to accurately predict the RUL of devices and device components based on rigorous statistical analysis data to reduce service costs by implementing condition-based maintenance.